Introduction
This article delves into the intricate art of data visualization and demystifies the process of creating, customizing, and interpreting time series line charts. Whether you're a seasoned data analyst or a budding enthusiast, join us as we navigate the essentials and intricacies of Matplotlib, equipping you with the tools to harness the power of time series data visualization.
What is time series line graph?
A time series line chart is a graphical representation of data points plotted against time. It is a powerful data visualization tool that helps us understand patterns, trends, and relationships in time-dependent data. Plotting data points on a line graph allows us to quickly identify any changes or fluctuations over time.
Importance of Time Series Line Charts in Visualization
Time series line charts play a crucial role in data visualization for several reasons. First of all, they allow us to view the information in our data. temporal patterns and trends, making it easy to identify any seasonality, trend or anomaly. This is particularly useful in finance, economics and weather forecasting, where it is essential to understand weather-dependent patterns.
Second, time series line charts help us analyze and compare multiple sets of time series data. Tracing various lines on the same graph allows us to quickly compare trends and patterns between different variables or categories.
Finally, time series line charts clearly and concisely represent complex data. Labels, legends, and annotations help us effectively communicate our findings to a broader audience. audience.
Also Read: A Complete Guide to Time Series Analysis and Forecasting
Getting started with Matplotlib
Installing Matplotlib
To get started with Matplotlib, you must first install it on your system. Installing Matplotlib is a simple process. You can install it using pip, which is the package installer for Python. Open your command prompt or terminal and type the following command:
Code:
pip install matplotlibOnce the installation is complete, you can verify it by importing the library in your Python script without errors.
Import the necessary libraries
After installing Matplotlib, import the necessary libraries into your Python script. Along with Matplotlib, you should also Import NumPy and Pandas libraries for data manipulation and analysis. Below is an example of how you can import these libraries:
Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pdBy importing these libraries, you will have access to several functions and methods that will help you create and customize your time series line charts.
Create a basic time series line graph
Here, we will learn how to create a basic time series line plot using Matplotlib. We will start by loading and preparing the data, then proceed to plot the linear graph of the time series. After that, we'll customize the plot by adding tags and titles.
Loading and preparing data
To create a time series line chart, we need a data set containing time-related information. We can use various data sources, such as CSV and Excel files, or even create a short data frame to plot values.
For example, let's say we have a data set that contains a week's worth of daily temperature readings. We can load this data into a pandas DataFrame and convert the date column to a datetime object.
Code:
import pandas as pd
# Create a DataFrame with date and temperature columns
data = {'date': ('2024-02-01', '2024-02-02', '2024-02-03', '2024-02-04', '2024-02-05', '2024-02-06', '2024-02-07'),
'temperature': (18, 17, 21, 20, 20, 19, 20)}
df = pd.DataFrame(data)
# Convert the date column to datetime object
df('date') = pd.to_datetime(df('date'))
df.head()Production:
Plot the linear diagram of the time series
Once the data is ready, we can use Matplotlib to create a time series line plot. Matplotlib's `plot()` function creates the line diagram.
Code:
import matplotlib.pyplot as plt
# Plot the timeseries line plot
plt.plot(df('date'), df('temperature'))
# Display the plot
plt.show()Production:
Customizing the plot
We can customize the time series line chart by adding grid lines, axis labels, legends and xticks using functions provided by Matplotlib.
Code:
import matplotlib.pyplot as plt
# Plot the timeseries line plot
plt.plot(df('date'), df('temperature'))
# Add grid lines
plt.grid(True)
# Add x-axis and y-axis labels
plt.xlabel('Date')
plt.ylabel('Temperature')
# Add a title
plt.title('Daily Temperature')
# Rotating the xticks
plt.xticks(rotation = 45)
# Display the plot
plt.show()Production:
Add tags and titles
To make the time series line chart more informative, we can add labels to the data points and a title to the chart. We can use Matplotlib's `annotate()` function to add tags and the `title()` function to add a title.
Code:
import matplotlib.pyplot as plt
# Plot the timeseries line plot
plt.plot(df('date'), df('temperature'))
# Add labels to the data points
for i in range(len(df)):
plt.annotate(df('temperature')(i), (df('date')(i), df('temperature')(i)))
# Add a title
plt.title('Daily Temperature')
# Display the plot
plt.xticks(rotation = 45)
plt.show()Production:
Improving Time Series Line Plots with Matplotlib
This section will explore several ways to improve our time series line plots using Matplotlib. we will learn to change line styles and colors, add grid lines and legends, and adjust axis limits and marks.
Change line styles and colors
We can customize line styles and colors to make our time series line charts more visually appealing. Matplotlib provides a wide range of options for this purpose. We can use line styles such as solid, dashed, dotted or dash-dot. In addition, we can choose between several colors. to make our plots more vibrant and distinguishable.
Below is an example of how we can change the line style and color in a time series line chart:
Code:
import matplotlib.pyplot as plt
# Create a figure and axis
fig, ax = plt.subplots()
# Plot the timeseries data with a dashed red line
ax.plot(df('date'), df('temperature'), linestyle="--", color="red")
# Add labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Temperature')
ax.set_title('Timeseries Line Plot')
# Show the plot
plt.xticks(rotation = 45)
plt.show()Production:
Add grid lines and legends
Grid lines can be useful in time series line plots to provide a reference for data points. We can easily add grid lines to our graphs using Matplotlib. Additionally, captions can be added to indicate the meaning of different plot lines.
Below is an example of how we can add grid lines and legends to a time series line chart:
Code:
import matplotlib.pyplot as plt
# Create a figure and axis
fig, ax = plt.subplots()
# Plot the timeseries data
ax.plot(df('date'), df('temperature'))
# Add gridlines
ax.grid(True)
# Add a legend
ax.legend(('Value'))
# Add labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Temperature')
ax.set_title('Timeseries Line Plot')
# Show the plot
plt.xticks(rotation = 45)
plt.show()Production:
Adjust axis limits and ticks
Grid lines can be useful in time series line charts to reference data points. We can easily add grid lines to our graphs using Matplotlib. Additionally, captions can be added to indicate the meaning of different plot lines.
Below is an example of how we can adjust the axis and tick limits on a time series line chart:
Code:
import pandas as pd
import matplotlib.pyplot as plt
# Create a DataFrame with date and temperature columns
data = {'date': ('2024-02-01', '2024-02-02', '2024-02-03', '2024-02-04', '2024-02-05', '2024-02-06', '2024-02-07'),
'temperature': (18, 17, 21, 20, 20, 19, 20)}
df = pd.DataFrame(data)
# Convert the date column to datetime object
df('date') = pd.to_datetime(df('date'))
# Create a figure and axis
fig, ax = plt.subplots()
# Plot the timeseries data
ax.plot(df('date'), df('temperature'))
# Set the x-axis limits
ax.set_xlim((pd.to_datetime('2024-02-01'), pd.to_datetime('2024-02-10')))
# Set the y-axis limits
ax.set_ylim((10, 25)) # Adjusted the y-axis limit for better visualization
# Add labels and title
ax.set_xlabel('Date')
ax.set_ylabel('Temperature')
ax.set_title('Timeseries Line Plot')
# Rotate the x-axis ticks
plt.xticks(rotation=45)
# Show the plot
plt.show()Production:
Conclusion
In this complete guide, we have learned how to create time series line plots using Matplotlib. We explore the process of importing and preprocessing time series data and the steps to plot the data using Matplotlib. Additionally, we improved our charts by changing line styles and colors, adding grid lines and legends, and adjusting axis limits and markings. We can create visually appealing and informative time series line plots for various applications by following these techniques..
